About the Role

We are seeking an experienced AI Solution Architect to lead the design and delivery of enterprise-scale AI solutions across complex business environments. The ideal candidate will have a strong background in solution architecture, hands-on experience with up-to-date AI technologies, and a deep understanding of AI security, governance, and operational best practices.

Key Responsibilities

- Design and deliver scalable AI solution architectures for large enterprise and telecommunications environments.
- Define end-to-end architectures for Generative AI, Advanced Retrieval-Augmented Generation (RAG), and Agentic AI solutions.
- Collaborate with business stakeholders, engineering teams, data scientists, and security teams to translate business requirements into AI-driven solutions.
- Evaluate and recommend appropriate AI models, frameworks, platforms, and integration patterns.
- Architect AI systems that meet enterprise requirements for scalability, reliability, security, governance, and observability.
- Provide technical leadership throughout the solution lifecycle, from discovery and design through implementation and production deployment.
- Establish architecture standards, best practices, and governance frameworks for AI solutions.

Required Experience & Skills
- Minimum 5+ years of Solution Architecture experience, including at least 1 year focused on AI solution architecture within enterprise-scale or telecommunications environments.
- Strong understanding of modern AI architectures, including:Retrieval-Augmented Generation (RAG)
- Advanced RAG patterns
- Agentic AI and multi-agent systems
- LLM application architecture
- AI orchestration and workflow automation
- Hands-on experience with AI agent development frameworks such as:LangChain
- LangGraph




- Semantic Kernel
- AutoGen or similar frameworks
- Experience with vector and graph database technologies, including:Pinecone
- Weaviate
- Milvus
- ChromaDB
- Neo4j
- Amazon Neptune or equivalent
- Knowledge of AI interoperability and communication protocols, including:Model Context Protocol (MCP)
- Agent-to-Agent (A2A) communication
- API-driven AI integrations
- Experience implementing AI observability, monitoring, and evaluation frameworks for production AI applications.
- Strong understanding of cloud platforms such as Azure, AWS, or Google Cloud and their AI service offerings.

AI Security & Governance
- Strong understanding of AI security principles, risks, and mitigation strategies.
- In-depth knowledge of the OWASP Top 10 for LLM Applications and AI-specific security controls.
- Familiarity with AI risk and governance frameworks such as:NIST AI Risk Management Framework (AI RMF)
- MITRE ATLAS
- Responsible AI principles
- Model governance and compliance standards
- Experience designing secure AI solutions with appropriate controls for privacy, data protection, model integrity, and prompt security.

Highly Desirable
- Telecommunications industry experience.
- Experience with AI platform engineering, MLOps, or LLMOps.
- Knowledge of AI evaluation frameworks, guardrails, and content safety controls.
- Experience with fine-tuning, model deployment, and agent orchestration platforms.
- Industry certifications in cloud architecture, AI, or cybersecurity.

What's on Offer
- Opportunity to shape enterprise AI strategy and architecture.
- Work on cutting-edge Generative AI and Agentic AI initiatives.
- Collaborate with senior technology and business leaders on high-impact transformation programs.
- Competitive salary and flexible working arrangements.

📌 Solution Architect - AI (Sydney)
🏢 N2S.Global
📍 Sydney